Add documentation about adding reasoning and indexing pipelines to the application (#138)

libs/ktem/ktem/app.py

@@ -73,11 +73,12 @@ class BaseApp:
         if getattr(settings, "KH_REASONINGS", None) is None:
             return
 
-        for name, value in settings.KH_REASONINGS.items():
+        for value in settings.KH_REASONINGS:
             reasoning_cls = import_dotted_string(value, safe=False)
-            reasonings[name] = reasoning_cls
+            rid = reasoning_cls.get_info()["id"]
+            reasonings[rid] = reasoning_cls
             options = reasoning_cls().get_user_settings()
-            self.default_settings.reasoning.options[name] = BaseSettingGroup(
+            self.default_settings.reasoning.options[rid] = BaseSettingGroup(
                 settings=options
             )
 

libs/ktem/ktem/pages/chat/events.py

@@ -128,7 +128,7 @@ async def chat_fn(chat_history, files, settings):
     pipeline = create_pipeline(settings, files)
     pipeline.set_output_queue(queue)
 
-    asyncio.create_task(pipeline(chat_input))
+    asyncio.create_task(pipeline(chat_input, chat_history))
     text, refs = "", ""
 
     while True:

libs/ktem/ktem/reasoning/simple.py

@@ -3,7 +3,7 @@ import logging
 import warnings
 from collections import defaultdict
 from functools import partial
-from typing import Iterator, Optional
+from typing import Optional
 
 import tiktoken
 from ktem.components import embeddings, get_docstore, get_vectorstore, llms
@@ -278,7 +278,7 @@ class AnswerWithContextPipeline(BaseComponent):
 
     async def run(  # type: ignore
         self, question: str, evidence: str, evidence_mode: int = 0
-    ) -> Document | Iterator[Document]:
+    ) -> Document:
         """Answer the question based on the evidence
 
         In addition to the question and the evidence, this method also take into
@@ -342,7 +342,9 @@ class FullQAPipeline(BaseComponent):
     evidence_pipeline: PrepareEvidencePipeline = PrepareEvidencePipeline.withx()
     answering_pipeline: AnswerWithContextPipeline = AnswerWithContextPipeline.withx()
 
-    async def run(self, question: str, **kwargs) -> Document:  # type: ignore
+    async def run(  # type: ignore
+        self, question: str, history: list, **kwargs  # type: ignore
+    ) -> Document:  # type: ignore
         docs = self.retrieval_pipeline(text=question)
         evidence_mode, evidence = self.evidence_pipeline(docs).content
         answer = await self.answering_pipeline(
@@ -455,3 +457,11 @@ class FullQAPipeline(BaseComponent):
                 "choices": main_llm_choices,
             },
         }
+
+    @classmethod
+    def get_info(cls) -> dict:
+        return {
+            "id": "simple",
+            "name": "Simple QA",
+            "description": "Simple QA pipeline",
+        }